IASM: A System for the Intelligent Active Surveillance of Malaria

Figure 1

(a) The overall architecture of the IASM. Users can select a location, year, and socioeconomic and environmental attributes using a Web UI. The information submitted by the user is then sent to the geographic information display and prediction engine models. The first model displays the location information on a map. Next, the prediction engine generates the prediction results using the input information and datasets. Then, the areas at high risk of infection are identified based on the prediction results via active surveillance. (b) Detailed structure of a PE module with a VCAP extension and some equations [11].

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